It is generally known that one type of spectroscopy can be defined as a study of electromagnetic wave interactions with material. When electromagnetic radiation such as light reaches the material, energetic transitions occur, transitions which are different according to the type of light. The near infrared (NIR) region (780-2500 nm) is situated between the red band of visible light and the mid infrared region. Molecular vibrations of hydrogen bonds, such as C—H, N—H, O—H and S—H, give birth to the NIR spectrum.
NIR spectroscopy offers many advantages such as no sample preparation, no sample destruction, fast data acquisition, and the use of optical fibers allows “online” analysis. Moreover, NIR spectra contain both physical and chemical information. Physical information can be granulometry, particles shape, polymorphism etc., and, on the other hand, chemical information can be the active pharmaceutical ingredient, moisture etc. However, each technique has its drawbacks:
In general, it is a problem that the NIR system must be calibrated. Optimisation and calibration are time-consuming tasks because the development of the predictive model has to take into account the use of a suitable reference method (e.g. chromatography, titration) to assign quantitative values of the chemical or physical parameter of interest for the spectral data. Moreover, due to the great quantity of physical and chemical information included in the NIR spectra, a visual interpretation is difficult. Basically, only a small piece of information is relevant for the objective investigated. For this purpose chemometric tools (e.g. MLR, PLS, PCR, ANN) are used to extract the significant information arising from the physical and chemical data using multivariate approaches. Chemometrics is the use of mathematical and statistical methodologies applied in general to chemical data. These tools include methods adapted for cleaning, classifying, interpreting and extracting information from data or signals. Two widely used chemometric tools for spectral analyses are mathematical pretreatment and regression methods. The first one consists in suppressing the biggest part of the information that is not directly linked to the chemical nature of the sample. Examples of mathematical pretreatments or data pretreatments include Savitzky-Golay smoothing filter, standard normal variate (SNV) and multiplicative scatter correction (MSC). The second one, i.e. the regression methods or data regression models, links a spectrum to a concentration of interest determined by the reference method, allowing the creation of a mathematical model that is useful for calibration purposes. Examples for regression methods include multiple linear regression (MLR), partial least squares (PLS) regression and artificial neuronal network (ANN), as described in D. A. Burns, E. W. Ciurczak, “Handbook of Near-Infrared Analysis”, Practical Spectroscopy Series Vol. 27, Marcel Dekker: New York, 2001.
In the pharmaceutical field, conformity analyses realized between batch production and batch release can be time consuming if all of them are performed after the manufacturing process. Thus, the concept of process analytical technology (PAT) is born, as described in American Food and Drug Administration (FDA), Guidance for Industry PAT-A Framework for Innovative Pharmaceutical Manufacturing and Quality Assurance, FDA, 2004. The aim of PAT is to enable monitoring each critical step of the fabrication in real time and, thus, reducing the batch release time. PAT gives also the opportunity to tune manufacturing parameters, and thus PAT allows avoiding the loss of batches. Regarding its non-invasive, non-destructive and fast data acquisition character, NIR spectroscopy is more and more associated with the concept of PAT.
Near infrared spectroscopy can be used to realize assays in the pharmaceutical field. The development of NIR system is generally split into 2 parts: calibration and validation of the predictive model. It is based on calibration data used to build calibration models regarding the chosen chemometric tools. After, an independent validation set of data is used for the validation of the selected model, respectively.
Original concepts of tolerance intervals based on the total error approach and the graph of the accuracy profile have been introduced to help the validation process, as described in the three Hubert Ph. et al. articles: Harmonization of strategies for the validation of quantitative analytical procedures—A SFSTP proposal—parts I-III, Journal of Pharmaceutical and Biomedical Analysis, 2004-2007. This approach can be applied on any type of analytical technique and is independent with regard to the matrix in which the analyte or substance of interest is analysed, such as in pharmaceutical formulations, biological fluids etc.
An accuracy index can be computed for each accuracy profile in order to resume all the information included in the accuracy profile in one “desirability” value. The accuracy index is therefore a basis to select the best calibration model only based on the calibration data.